Unifying Outputs from Thousands of Parallel Agents in Forecasting
they_will · reddit · 2026-07-28
The author shares insights on optimizing large-scale agentic workloads (scaling to thousands of agents per request), where the core challenge is ensuring consistency across all parallel workers (e.g., unified methodology, units, and source quality).
For the specific use case of forecasting, the author built a more tractable solution (FutureSearch) with several advantages:
- Task homogeneity: Forecasts share a similar structure, making internal benchmarks highly representative.
- Shared world model: All forecasts attempt to understand the same world, allowing past forecasts to inform new ones.
- Localized consistency: Consistency requirements are mostly confined to data subsets.
- Post-processing architecture: Running as a post-processing step avoids repeating expensive raw agent runs during experiments, saving significant inference costs.
The system currently ranks #1 in several Metaculus forecasting tournaments and has a proven track record in real-money markets.
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